Yuval Shahar, M.D., Ph.D.

1. Judgment and Decision Making in Information SystemsIntroduction:Decision Analysis and Human Judgment Yuval Shahar, M.D., Ph.D.

2. Decision Making • Life involves making decisions! • Decision makers require guidelines and expert support • Most important decisions involve • multiple uncertainties • multiple outcomes, which can often be evaluated using multiple attributes • Multiple decision-making stages • information gathering at every stage • Examples in everyday life include business, government policy, medicine, law, and personal decisions

3. Decision Analysis • Requires modeling the decision • Several effective graphical modeling methods • Provides tools for quantitative analysis of decisions with multiple uncertainties and/or conflicting objectives • Provides decision makers with insight, not necessarily a solution • Example: Multi-way sensitivity analysis • Benefits from using computational tools

4. Decision Making in Medicine: a Typical Example • A 35 yrs old patient has Hodgkin's Lymphoma, probably (80%) stage II by physical examination and X-rays • She needs to decide with her doctor which of the following options she should chose: • start radiotherapy immediately (typical stage II therapy) • start chemotherapy immediately (typical stage III therapy; implies more side effects) • undergo explorative laparotomy (a major operation that explores the abdominal cavity) to find out if she has stage II or stage III disease, then decide on radiotherapy or chemotherapy, using the new information

5. Personal Decision Making:The Party Problem • Joseph K. invites his friends to a party, but needs to decide on the location: • Outdoors (O) on the grass (completely open) • On the Porch (P) (covered above, open on sides) • Inside (I) the living room • If the weather is sunny (S), outdoors is best, followed by Porch and Indoors; if it rains (R), the living room is best, followed by Porch and Outdoors

6. Rules of Actional Thought • Ronald Howard’s version of the decision making axioms proposed by John von Neumann and Oscar Morgenstern in their classic work on game theory (1944, 1947) • Simple, intuitive guidelines to follow when making decisions • A set of five rational, consistent rules for a normative decision maker to follow

7. The Probability Rule • Decision makers use elemental and compound possibilities (e.g., rain; Sun & Porch) and probabilities to provide distinctions and information that characterize deals • The clarity test: Crucial for making clairvoyance meaningful and useful • Relevance of events • Mutual exclusion of elemental possibilities • Collective exhaustion of elemental possibilities

8. The Order Rule • Prospects (values of outcomes of deals) can be arranged in a (weakly) descending order from best to worst • The order of prospects is consistent and transitive • A>B, B>C, => A>C • Nontransitive orders lead to a “money pump”

9. The Equivalence Rule • If A>B>C, then there is a number 0<p<1 such that the decision maker is indifferent between getting prospect B for sure, and receiving a deal with probability p of getting A and probability 1-p of getting C • P is the preference probability of this model • B is the certain equivalent of the A,C deal

10. Preference Probabilities P 1 A  B 1-P C

11. The Substitution Rule • The decision maker has to be indifferent between receiving a prospect and any deal for which that prospect is a certain equivalent • B can be substituted for the A,C deal in any situation • Implies treatment of preference probabilities as probabilities that might lead to action

12. The Choice Rule • If the prospect ordering includes D>E, and there are two deals with outcomes D,E, the decision maker must prefer the deal in which the probability of getting D is higher • The only specific-action rule • Simply states that decision makers follow their preferences, whatever these are

13. Decision Models • Normative models • Decision Trees • Influence Diagrams • Belief Networks • (Markov Chains) • Descriptive models • Fallacies and biases in human decision making and judgment • The five rules are often violated in practice • Prospect theory (Tversky and Kahnemann)

14. Decision Modeling byDecision Trees • A convenient way to explicitly show • the order and relationships of possible decisions • Uncertain (chance) outcomes of decisions • outcome results and their utilities (values) • Enable computation of the decision that maximizes expected utility

15. Decision Trees Conventions Decision node Chance node Result node Information link Influence link

16. The Party Problem Decision Tree S R O S P R I S R

17. A Generic Decision Treefor a Medical Therapy Decison

18. Decision Trees: an HIV Example Decision node Chance node

19. Decision Modeling by Influence Diagrams: Node Conventions Chance node Decision node Utility node

20. Link Semanticsin Influence Diagrams Dependence link Information link Influence link

21. Influence Diagrams: An HIV Example

22. The Structure of Influence Diagram Links

23. Belief Networks (Bayesian/Causal Probabilistic/Probabilistic Networks, etc) Influence diagrams (DAGs) without decision and utility nodes Disease Gender Sinusitis Fever Runny nose Headache

24. Link Semantics in Belief Networks Dependence Independence B Conditional independence of B and C, given A A C

25. Advantages of Influence Diagrams and Belief Networks • Excellent modeling tool that supports acquisition from domain experts • Intuitive semantics (e.g., information and influence links) • Explicit representation of dependencies • very concise representation of large decision models • “Anytime” algorithms available (using probability theory) to compute the distribution of values at any node given the values of any subset of the nodes (e.g., at any stage of information gathering) • Explicit support for value of information computations

26. Disadvantages of Influence Diagrams and Belief Networks • Explicit representation of dependencies often requires acquisition of joint probability distributions (P(A|B,C)) • Computation in general intractable (NP hard) • Order of decisions and relations between decisions and available information might be obscured

27. Examples of Successful Belief-Network and/or Influence Diagram Applications • In clinical medicine: • Pathological diagnosis at the level of a subspecialized medical expert (Pathfinder) • Endocrinological diagnosis (NESTOR) • In bioinformatics: • Recognition of meaningful sites and features in DNA sequences • Educated guess of tertiary structure of proteins

28. Markov Models • A probabilistic version of finite state machines/automata (FSM/FSA) where each node is a variable in the probability space • Each variable is independent of its predecessors, given its parents • A common method for simulation of changes of state over time P1,2 P2,3 S1 S2 S3 P2,1